modEnrichr: a suite of gene set enrichment analysis tools for model organisms

被引:52
作者
Kuleshov, Maxim V. [1 ]
Diaz, Jennifer E. L. [2 ]
Flamholz, Zachary N. [1 ]
Keenan, Alexandra B. [1 ]
Lachmann, Alexander [1 ]
Wojciechowicz, Megan L. [1 ]
Cagan, Ross L. [2 ]
Ma'ayan, Avi [1 ]
机构
[1] Icahn Sch Med Mt Sinai, Mt Sinai Ctr Bioinformat, Dept Pharmacol Sci, One Gustave L Levy Pl,Box 1215, New York, NY 10029 USA
[2] Icahn Sch Med Mt Sinai, Dept Cell Dev & Regenerat Biol, One Gustave L Levy Pl,Box 1020, New York, NY 10029 USA
关键词
WEB SERVER; PREDICTION; ONTOLOGY; ANNOTATION; DATABASE; PROTEIN;
D O I
10.1093/nar/gkz347
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
High-throughput experiments produce increasingly large datasets that are difficult to analyze and integrate. While most data integration approaches focus on aligning metadata, data integration can be achieved by abstracting experimental results into gene sets. Such gene sets can be made available for reuse through gene set enrichment analysis tools such as Enrichr. Enrichr currently only supports gene sets compiled from human and mouse, limiting accessibility for investigators that study other model organisms. modEnrichr is an expansion of Enrichr for four model organisms: fish, fly, worm and yeast. The gene set libraries within FishEnrichr, FlyEnrichr, WormEnrichr and YeastEnrichr are created from the Gene Ontology, mRNA expression profiles, GeneRIF, pathway databases, protein domain databases and other organism-specific resources. Additionally, libraries were created by predicting gene function from RNA-seq co-expression data processed uniformly from the gene expression omnibus for each organism. The modEnrichr suite of tools provides the ability to convert gene lists across species using an ortholog conversion tool that automatically detects the species. For complex analyses, modEnrichr provides API access that enables submitting batch queries. In summary, modEnrichr leverages existing model organism databases and other resources to facilitate comprehensive hypothesis generation through data integration.
引用
收藏
页码:W183 / W190
页数:8
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